Several Examples of visualisation by Kohonen's SOM

The result of visualization for classical sample (2 spirals) by Kohonen's Self Organizing Map is presented.
The task is to learn to discriminate between two sets of training points which lie on two distinct spirals in the x-y plane. These spirals coil three times around the origin and around one another. This appears to be a very difficult task for back-propagation networks and their relatives.
Problems like this one, whose inputs are points on the 2-D plane, are interesting because we can display the 2-D "receptive field" of any unit in the network.
The task is to train on the 194 I/O pairs until the learning system can produce the correct output for all of the inputs.

SOM with such parameters:
size map - 30, Neighbor function - Cone, Radius - 10, Epochs - 250



SOM with the parameters:
size map - 30, Neighbor function - Cos, Radius - 5, Epochs - 100

Short Lecture about Perceptron and Kohonen's SOM

It is my short lecture in classical neural networks. It was delivered to students from School of Mathematics and Systems Engineering, Vaxjo University in September, 2008.

Outline:
- Supervised Learning
- Perceptrons: Multilayer Perceptron
- Back-Propagation Training Algorithm
- Self-organizing map
- Data visualization techniques using the SOM

Fifth International Workshop on Artificial Neural Networks and Intelligent Information Processing

I can recommend to visit this Workshop.

http://www.icinco.org/ANNIIP.htm